Database activity monitoring (DAM) is increasingly essential for organizations handling data, given its role in optimizing mission-critical processes and timely issue detection. The global DAM software market is expected to grow by 15% by 2029, largely driven by heightened data privacy regulations and rising cyber-attack risks. DAM tools leverage machine learning to enhance their solutions, providing continuous, real-time tracking of user actions and database changes, and sending alerts for suspicious activities. The importance of DAM is particularly pronounced in the context of remote work, where personal devices and potentially insecure networks are used. The article discusses how to build a custom DAM workflow using n8n to monitor a Postgres database, where it generates and checks sensor data, sending SMS alerts via Twilio when outliers are detected. This setup exemplifies how DAM can increase productivity, improve database performance, and ensure compliance with data privacy standards.